Articles | Volume 9, issue 1
https://doi.org/10.5194/ascmo-9-67-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-9-67-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
05 Jun 2023
| 05 Jun 2023
Statistical modeling of the space–time relation between wind and significant wave height
Said Obakrim
CORRESPONDING AUTHOR
Univ. Rennes CNRS, IRMAR – UMR 6625, 35000 Rennes, France
Ifremer, RDT, 29280 Plouzané, France
Pierre Ailliot
Laboratoire de Mathématiques de Bretagne Atlantique, Univ. Brest CNRS, UMR 6205, 29200 Brest, France
Valérie Monbet
Univ. Rennes CNRS, IRMAR – UMR 6625, 35000 Rennes, France
Nicolas Raillard
Ifremer, RDT, 29280 Plouzané, France
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Timothy DelSole and Michael K. Tippett
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Julie Bessac and Philippe Naveau
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 53–71, https://doi.org/10.5194/ascmo-7-53-2021, https://doi.org/10.5194/ascmo-7-53-2021, 2021
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Eric Gilleland
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 13–34, https://doi.org/10.5194/ascmo-7-13-2021, https://doi.org/10.5194/ascmo-7-13-2021, 2021
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Verifying high-resolution weather forecasts has become increasingly complicated,
and simple, easy-to-understand summary measures are a good alternative. Recent work has demonstrated some common pitfalls with many such summaries. Here, new summary measures are introduced that do not suffer from these drawbacks, while still providing meaningful information.
Thomas Patrick Leahy
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 1–11, https://doi.org/10.5194/ascmo-7-1-2021, https://doi.org/10.5194/ascmo-7-1-2021, 2021
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This study looked at estimating damages caused by hurricanes in the United States. It assessed the relationship between the maximum wind speed at landfall and the resulting damage caused. The study found that the complex processes that determine the size of the damages inflicted could be estimated using this simple relationship. This work could be used to examine how often extreme damage events are likely to occur and the impact of stronger hurricane winds on the US Atlantic and Gulf coasts.
Yoann Robin and Aurélien Ribes
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 205–221, https://doi.org/10.5194/ascmo-6-205-2020, https://doi.org/10.5194/ascmo-6-205-2020, 2020
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We have developed a new statistical method to describe how a severe weather event, such as a heat wave, may have been influenced by climate change. Our method incorporates both observations and data from various climate models to reflect climate model uncertainty. Our results show that both the probability and the intensity of the French July 2019 heatwave have increased significantly in response to human influence. We find that this heat wave might not have been possible without climate change.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 159–175, https://doi.org/10.5194/ascmo-6-159-2020, https://doi.org/10.5194/ascmo-6-159-2020, 2020
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Scientists often are confronted with the question of whether two time series are statistically distinguishable. This paper proposes a test for answering this question. The basic idea is to fit each time series to a time series model and then test whether the parameters in that model are equal. If a difference is detected, then new ways of visualizing those differences are proposed, including a clustering technique and a method based on optimal initial conditions.
Joshua North, Zofia Stanley, William Kleiber, Wiebke Deierling, Eric Gilleland, and Matthias Steiner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 79–90, https://doi.org/10.5194/ascmo-6-79-2020, https://doi.org/10.5194/ascmo-6-79-2020, 2020
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Very short-term forecasting, called nowcasting, is used to monitor storms that pose a significant threat to people and infrastructure. These threats could include lightning strikes, hail, heavy precipitation, strong winds, and possible tornados. This paper proposes a fast approach to nowcasting lightning threats using simple statistical methods. The proposed model results in fast nowcasts that are more accurate than a competitive, computationally expensive, approach.
Ola Haug, Thordis L. Thorarinsdottir, Sigrunn H. Sørbye, and Christian L. E. Franzke
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 1–12, https://doi.org/10.5194/ascmo-6-1-2020, https://doi.org/10.5194/ascmo-6-1-2020, 2020
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Trends in gridded temperature data are commonly assessed independently for each grid cell, ignoring spatial coherencies. This may severely affect the interpretation of the results. This article proposes a space–time model for temperatures that allows for joint assessments of the trend across locations. In a case study of summer season trends in Europe, it is found that the region with a significant trend under spatial coherency is vastly different from that under independent assessments.
Alexis Hannart
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 161–171, https://doi.org/10.5194/ascmo-5-161-2019, https://doi.org/10.5194/ascmo-5-161-2019, 2019
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In climate change attribution studies, one often seeks to maximize a signal-to-noise ratio, where the
signalis the anthropogenic response and the
noiseis climate variability. A solution commonly used in D&A studies thus far consists of projecting the signal on the subspace spanned by the leading eigenvectors of climate variability. Here I show that this approach is vastly suboptimal – in fact, it leads instead to maximizing the noise-to-signal ratio. I then describe an improved solution.
Moritz N. Lang, Georg J. Mayr, Reto Stauffer, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 115–132, https://doi.org/10.5194/ascmo-5-115-2019, https://doi.org/10.5194/ascmo-5-115-2019, 2019
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Accurate wind forecasts are of great importance for decision-making processes in today's society. This work presents a novel probabilistic post-processing method for wind vector forecasts employing a bivariate Gaussian response distribution. To capture a possible mismatch between the predicted and observed wind direction caused by location-specific properties, the approach incorporates a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction.
X. Joey Wang, John R. J. Thompson, W. John Braun, and Douglas G. Woolford
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 57–66, https://doi.org/10.5194/ascmo-5-57-2019, https://doi.org/10.5194/ascmo-5-57-2019, 2019
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This paper presents the analysis of data from small-scale laboratory experimental smouldering fires that were digitally video-recorded. The video images of these fires bear a resemblance to remotely sensed images of wildfires and provide an opportunity to fit and assess a spatial model for fire spread that attempts to account for uncertainty in fire growth. We found that the fitting method is feasible, and the spatial model provides a suitable mathematical for the fire spread process.
Robin Tokmakian and Peter Challenor
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 17–35, https://doi.org/10.5194/ascmo-5-17-2019, https://doi.org/10.5194/ascmo-5-17-2019, 2019
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As an example of how to robustly determine climate model uncertainty, the paper describes an experiment that perturbs the initial conditions for the ocean's temperature of a climate model. A total of 30 perturbed simulations are used (via an emulator) to estimate spatial uncertainties for temperature and precipitation fields. We also examined (using maximum covariance analysis) how ocean temperatures affect air temperatures and precipitation over land and the importance of feedback processes.
Thorsten Simon, Georg J. Mayr, Nikolaus Umlauf, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 1–16, https://doi.org/10.5194/ascmo-5-1-2019, https://doi.org/10.5194/ascmo-5-1-2019, 2019
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Lightning in Alpine regions is associated with events such as thunderstorms,
extreme precipitation, high wind gusts, flash floods, and debris flows.
We present a statistical approach to predict lightning counts based on
numerical weather predictions. Lightning counts are considered on a grid
with 18 km mesh size. Skilful prediction is obtained for a forecast horizon
of 5 days over complex terrain.
Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 53–63, https://doi.org/10.5194/ascmo-4-53-2018, https://doi.org/10.5194/ascmo-4-53-2018, 2018
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Millions of people worldwide are at a risk of coastal flooding, and this number will increase as the climate continues to change. This study analyzes how climate change affects future flood hazards. A new model that uses multiple climate variables for flood hazard is developed. For the case study of Norfolk, Virginia, the model predicts 23 cm higher flood levels relative to previous work. This work shows the importance of accounting for climate change in effectively managing coastal risks.
Amy Braverman, Snigdhansu Chatterjee, Megan Heyman, and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 93–105, https://doi.org/10.5194/ascmo-3-93-2017, https://doi.org/10.5194/ascmo-3-93-2017, 2017
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In this paper, we introduce a method for expressing the agreement between climate model output time series and time series of observational data as a probability value. Our metric is an estimate of the probability that one would obtain two time series as similar as the ones under consideration, if the climate model and the observed series actually shared the same underlying climate signal.
Joshua P. French, Seth McGinnis, and Armin Schwartzman
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 67–92, https://doi.org/10.5194/ascmo-3-67-2017, https://doi.org/10.5194/ascmo-3-67-2017, 2017
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We assess the mean temperature effect of global and regional climate model combinations for the North American Regional Climate Change Assessment Program using varying classes of linear regression models, including possible interaction effects. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We conclusively show that accounting for multiple comparisons is important for making proper inference.
László Varga and András Zempléni
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 55–66, https://doi.org/10.5194/ascmo-3-55-2017, https://doi.org/10.5194/ascmo-3-55-2017, 2017
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This paper proposes a new generalisation of the block bootstrap methodology, which allows for any positive real number as expected block size. We use this bootstrap for determining the p values of a homogeneity test for copulas. The methods are applied to a temperature data set - we have found some significant changes in the dependence structure between the standardised temperature values of pairs of observation points within the Carpathian Basin.
Andrew Poppick, Elisabeth J. Moyer, and Michael L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 33–53, https://doi.org/10.5194/ascmo-3-33-2017, https://doi.org/10.5194/ascmo-3-33-2017, 2017
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We show that ostensibly empirical methods of analyzing trends in the global mean temperature record, which appear to de-emphasize assumptions, can nevertheless produce misleading inferences about trends and associated uncertainty. We illustrate how a simple but physically motivated trend model can provide better-fitting and more broadly applicable results, and show the importance of adequately characterizing internal variability for estimating trend uncertainty.
John Tipton, Mevin Hooten, and Simon Goring
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 1–16, https://doi.org/10.5194/ascmo-3-1-2017, https://doi.org/10.5194/ascmo-3-1-2017, 2017
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We present a statistical framework for the reconstruction of historic temperature patterns from sparse, irregular data collected from observer stations. A common statistical technique for climate reconstruction uses modern era data as a set of temperature patterns that can be used to estimate the spatial temperature patterns. We present a framework for exploration of different assumptions about the sets of patterns used in the reconstruction while providing statistically rigorous estimates.
Georgina Davies and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 155–169, https://doi.org/10.5194/ascmo-2-155-2016, https://doi.org/10.5194/ascmo-2-155-2016, 2016
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Sea surface temperature (SST) is a key component of global climate models, particularly in the tropical Pacific Ocean where El Niño and La Nina events have worldwide implications. In our paper, we analyse monthly SSTs in the Niño 3.4 region and find a transformation that removes a spatial mean-variance dependence for each month. For 10 out of 12 months in the year, the transformed monthly time series gave more accurate or as accurate forecasts than those from the untransformed time series.
Eric Gilleland, Melissa Bukovsky, Christopher L. Williams, Seth McGinnis, Caspar M. Ammann, Barbara G. Brown, and Linda O. Mearns
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 137–153, https://doi.org/10.5194/ascmo-2-137-2016, https://doi.org/10.5194/ascmo-2-137-2016, 2016
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Several climate models are evaluated under current climate conditions to determine how well they are able to capture frequencies of severe-storm environments (conditions conducive for the formation of hail storms, tornadoes, etc.). They are found to underpredict the spatial extent of high-frequency areas (such as tornado alley), as well as underpredict the frequencies in the areas.
Whitney K. Huang, Michael L. Stein, David J. McInerney, Shanshan Sun, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, https://doi.org/10.5194/ascmo-2-79-2016, 2016
Sergei N. Rodionov
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 63–78, https://doi.org/10.5194/ascmo-2-63-2016, https://doi.org/10.5194/ascmo-2-63-2016, 2016
David Bolin, Arnoldo Frigessi, Peter Guttorp, Ola Haug, Elisabeth Orskaug, Ida Scheel, and Jonas Wallin
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 39–47, https://doi.org/10.5194/ascmo-2-39-2016, https://doi.org/10.5194/ascmo-2-39-2016, 2016
Julie Bessac, Pierre Ailliot, Julien Cattiaux, and Valerie Monbet
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 1–16, https://doi.org/10.5194/ascmo-2-1-2016, https://doi.org/10.5194/ascmo-2-1-2016, 2016
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Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes. We also discuss the relative advantages of hidden and observed regime-switching models.
E. M. Schliep, A. E. Gelfand, and D. M. Holland
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 59–74, https://doi.org/10.5194/ascmo-1-59-2015, https://doi.org/10.5194/ascmo-1-59-2015, 2015
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Short summary
There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the US motivates the need for advanced statistical models to predict air quality metrics. We propose a statistical model that jointly models ground-monitoring station data and satellite-obtained data allowing for temporal and spatial misalignment, missingness, and spatially and temporally varying correlation to enhance prediction of particulate matter.
R. Philbin and M. Jun
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 29–44, https://doi.org/10.5194/ascmo-1-29-2015, https://doi.org/10.5194/ascmo-1-29-2015, 2015
T. K. Doan, J. Haslett, and A. C. Parnell
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 15–27, https://doi.org/10.5194/ascmo-1-15-2015, https://doi.org/10.5194/ascmo-1-15-2015, 2015
W. B. Leeds, E. J. Moyer, and M. L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 1–14, https://doi.org/10.5194/ascmo-1-1-2015, https://doi.org/10.5194/ascmo-1-1-2015, 2015
Cited articles
Accensi, M. and Maisondieu, C.:
HOMERE. Ifremer Laboratoire Comportement des Structures en Mer, Ifremer Laboratoire
Spatial et In terfaces Air Mer, [data set], https://doi.org/10.12770/cf47e08d-1455-4254-955e-d66225c9dc90, 2015. a
Anderson, D., Rueda, A., Cagigal, L., Antolinez, J., Mendez, F., and Ruggiero,
P.: Time-varying emulator for short and long-term analysis of coastal flood
hazard potential, J. Geophys. Res.-Oceans, 124, 9209–9234,
2019. a
Ardhuin, F., Hanafin, J., Quilfen, Y., Chapron, B., Queffeulou, P., Obrebski,
M., Sienkiewicz, J., and Vandemark, D.: Calibration of the IOWAGA global wave
hindcast (1991–2011) using ECMWF and CFSR winds, in: Proceedings of the 2011
International Workshop on Wave Hindcasting and Forecasting and 3rd Coastal
Hazard Symposium, Kona, HI, USA, November 2014, vol. 30, 2011. a
Ardhuin, F., Stopa, J. E., Chapron, B., Collard, F., Husson, R., Jensen, R. E.,
Johannessen, J., Mouche, A., Passaro, M., Quartly, G. D., Swail, V., and Young, I.: Observing
sea states, Front. Mar. Sci., 124, https://doi.org/10.3389/fmars.2019.00124, 2019. a
Barnston, A. G. and Livezey, R. E.: Classification, seasonality and persistence
of low-frequency atmospheric circulation patterns, Mon. Weather Rev.,
115, 1083–1126, 1987. a
Cagigal, L., Rueda, A., Anderson, D., Ruggiero, P., Merrifield, M. A.,
Montaño, J., Coco, G., and Méndez, F. J.: A multivariate, stochastic,
climate-based wave emulator for shoreline change modelling, Ocean Model.,
154, 101695, https://doi.org/10.1016/j.ocemod.2020.101695, 2020. a
Camus, P., Menendez, M., Mendez, F. J., Izaguirre, C., Espejo, A., Canovas, V.,
Perez, J., Rueda, A., Losada, I. J., and Medina, R.: A weather-type
statistical downscaling framework for ocean wave climate, J. Geophys. Res.-Oceans, 119, 7389–7405, 2014b. a
Camus, P., Rueda, A., Méndez, F. J., and Losada, I. J.: An
atmospheric-to-marine synoptic classification for statistical downscaling
marine climate, Ocean Dynam., 66, 1589–1601, 2016. a
Casas-Prat, M., Wang, X. L., and Sierra, J. P.: A physical-based statistical
method for modeling ocean wave heights, Ocean Model., 73, 59–75, 2014. a
Charles, E., Idier, D., Thiébot, J., Le Cozannet, G., Pedreros, R.,
Ardhuin, F., and Planton, S.: Present wave climate in the Bay of Biscay:
spatiotemporal variability and trends from 1958 to 2001, J. Climate,
25, 2020–2039, 2012. a
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E.,
Vose, R. S., Rutledge, G., Bessemoulin, P., Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N.,
Groisman, P. Y., Jones, P. D., Kruk, M., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, Ø.,
Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J.: The Twentieth Century
Reanalysis Project, Q. J. Roy. Meteor. Soc., 137, 1–28, https://doi.org/10.1002/qj.776, 2011 (data available at : https://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/nao.shtml, last access: 31 May 2023). a
Costa, W., Idier, D., Rohmer, J., Menendez, M., and Camus, P.: Statistical
Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type
Downscaling Model, Journal of Marine Science and Engineering, 8, 1028, https://doi.org/10.3390/jmse8121028, 2020. a
Hasselmann, K. F., Barnett, T. P., Bouws, E., Carlson, H., Cartwright, D. E.,
Eake, K., Euring, J., Gicnapp, A., Hasselmann, D., Kruseman, P., and Meerburg, A.:
Measurements of wind-wave growth and swell decay during the Joint North Sea
Wave Project (JONSWAP), Ergaenzungsheft zur Deutschen Hydrographischen
Zeitschrift, Reihe A, https://hdl.handle.net/21.11116/0000-0007-DD3C-E, 1973. a
Hegermiller, C., Antolinez, J. A., Rueda, A., Camus, P., Perez, J., Erikson,
L. H., Barnard, P. L., and Mendez, F. J.: A multimodal wave spectrum–based
approach for statistical downscaling of local wave climate, J.
Phys. Oceanogr., 47, 375–386, 2017. a
Hemer, M. A., Wang, X. L., Weisse, R., and Swail, V. R.: Advancing wind-waves
climate science: The COWCLIP project, B. Am. Meteorol.
Soc., 93, 791–796, 2012. a
Hessami, M., Gachon, P., Ouarda, T. B., and St-Hilaire, A.: Automated
regression-based statistical downscaling tool,
Environ. Modell. Softw., 23, 813–834, 2008. a
Mahajan, V., Jain, A. K., and Bergier, M.: Parameter estimation in marketing
models in the presence of multicollinearity: An application of ridge
regression, J. Marketing Res., 14, 586–591, 1977. a
Mori, N., Shimura, T., Yasuda, T., and Mase, H.: Multi-model climate
projections of ocean surface variables under different climate
scenarios–Future change of waves, sea level and wind, Ocean Eng.,
71, 122–129, 2013. a
Obakrim, S., Ailliot, P., Monbet, V., and Raillard, N.: Statistical
downscaling of significant wave height: code for data preparation and model, Zenodo [data set],
https://doi.org/10.5281/zenodo.5845250, 2022a. a
Obakrim, S., Ailliot, P., Monbet, V., and Raillard, N.: Statistical
downscaling of significant wave height: data, Zenodo [data set], https://doi.org/10.5281/zenodo.5845423,
2022b. a
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., and Liu, H.: The NCEP climate forecast
system reanalysis, B. Am. Meteorol. Soc., 91,
1015–1058, 2010 (data available at: https://climatedataguide.ucar.edu/climate-data/climate-forecast-system-reanalysis-cfsr, last access: 31 May 2023). a, b
Tolman, H. L.: User manual and system documentation of WAVEWATCH III TM
version 3.14, Technical note, MMAB Contribution, 276, 220 pp., 2009. a
Tracy, B., Devaliere, E., Hanson, J., Nicolini, T., and Tolman, H.: Wind sea
and swell delineation for numerical wave modeling, in: 10th international
workshop on wave hindcasting and forecasting & coastal hazards symposium,
JCOMM Tech. Rep, 11 November 2007, North Shore, Oahu, Hawaii, vol. 41, p. 1442, 2007. a
van Wieringen, W. N.: Lecture notes on ridge regression, arXiv [preprint],
https://doi.org/10.48550/arXiv.1509.09169, 2015. a
Wang, X. L., Swail, V. R., and Cox, A.: Dynamical versus statistical
downscaling methods for ocean wave heights, Int. J.
Climatol., 30, 317–332,
2010. a
Young, I. R.: Wind generated ocean waves, Elsevier, 1999. a
Short summary
Ocean wave climate has a significant impact on human activities, and its understanding is of socioeconomic and environmental importance. In this study, we propose a statistical model that predicts wave heights in a location in the Bay of Biscay. The proposed method allows us to understand the spatiotemporal relationship between wind and waves and predicts well both wind seas and swells.
Ocean wave climate has a significant impact on human activities, and its understanding is of...
